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CS-526
17 - 18 February
Notes Rodrigo Veiga I
Notes Rodrigo Veiga I
Click
lt_notes_pac_learning_RV1.pdf
link to view the file.
◄ Discussion and questions
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Discussion and questions
Notes Rodrigo Veiga II
PAC learning framework (notes N.M)
Finite classes and uniform convergence (notes N.M)
solution 1
No free lunch theorem
extrahmw-Hoeffding.pdf
solution 2
Learning infinite classes I
Homework 3 graded
Submission for HW3
solution-hmw-3-graded
Learning infinite classes II (VCdim)
Part I - Bias Viariance Decomposition and Double Descent
Moore Penrose hmw
Moore Penrose solution
Homework 4
Solution Homework 4
Additional exercises on PAC learning and VC dimension
Solution to Extra HW
solution hmw 4
Part II - Double descent phenomenon and derivation for the weak features model
Two models of weak features by Belkin, Hsu, Xu
Homework 5
Homework 5 solution
Gradient descent
Stochastic gradient descent
homework 6 graded
Submission for HW6
Notes Rodrigo Veiga II ►
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